Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly with the developer.
You’ll find lots of articles specialized in machine learning algorithms. This is an attempt to generate a “helicopter view” description of the way these algorithms are applied in different business areas. A list is not a comprehensive set of course.
The 1st point is ML algorithms will help people by helping these to find patterns or dependencies, which are not visible by way of a human.
Numeric forecasting looks like it’s one of the most recognized area here. For a long period computers were actively employed for predicting the behavior of economic markets. Most models were developed prior to 1980s, when stock markets got usage of sufficient computational power. Later these technologies spread with other industries. Since computing power is affordable now, it can be used by even small companies for all those forms of forecasting, like traffic (people, cars, users), sales forecasting plus much more.
Anomaly detection algorithms help people scan a lot of data and identify which cases needs to be checked as anomalies. In finance they can identify fraudulent transactions. In infrastructure monitoring they generate it very easy to identify challenges before they affect business. It’s used in manufacturing quality control.
The main idea is basically that you should not describe each type of anomaly. You give a large list of different known cases (a learning set) to the system and system put it on for anomaly identifying.
Object clustering algorithms allows to group big volume of data using wide range of meaningful criteria. A person can’t operate efficiently with over few countless object with a lot of parameters. Machine can perform clustering extremely effective, for instance, for patrons / leads qualification, product lists segmentation, customer support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides us chance to be efficient a lot more important customers or users through providing them the key they need, even when they have not contemplated it before. Recommendation systems works really bad in most of services now, however, this sector will probably be improved rapidly quickly.
The other point is that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing on this information (i.e. study on people) and apply this rules acting rather than people.
First of all that is about all sorts of standard decisions making. There are a lot of activities which require for standard actions in standard situations. People have the “standard decisions” and escalate cases which aren’t standard. There aren’t any reasons, why machines can’t accomplish that: documents processing, phone calls, bookkeeping, first line customer support etc.
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